Descriptive Statistics

Agenda

Frequency Distributions
Central Tendency
Variability

Readings

Being a Statistician Means Never Having to Say You’re Certain

\[ \definecolor{mathBlack}{RGB}{0,0,0} \definecolor{mathOrange}{RGB}{253, 126, 20} \definecolor{mathLightGreen}{RGB}{32, 201, 151} \definecolor{mathGreen}{RGB}{24, 188, 156} \definecolor{mathYellow}{RGB}{253, 156, 18} \definecolor{mathBlue}{RGB}{52, 152, 219} \definecolor{mathRed}{RGB}{231, 76, 60} \definecolor{mathPurple}{RGB}{111, 66, 193} \]

Frequency Distributions

FREQUENCY DISTRIBUTION
RELATIVE FREQUENCY DISTRIBUTION

PROPORTION
PERCENTAGE
CUMULATIVE
RATE

BAR GRAPH
HISTOGRAM
LINE GRAPH
STATISTICAL MAP

Objectives

Calculate proportions and percentages
Construct and analyze frequency, percentage, and cumulative distributions

DISTRIBUTION

Shows all the possible values (or intervals) of the data and how often they occur.

FREQUENCY DISTRIBUTION

A table reporting the number of observations falling into each category of the variable.

Tab A
Tab B
Tab C

Table 1. Attitudes about sex before marriage

premarsx	n
always wrong	357
almost always wrong	122
wrong only sometimes	258
not wrong at all	1,378
Total	2,115

Survey question: There’s been a lot of discussion about the way morals and attitudes about sex are changing in this country. If a man and woman have sex relations before marriage, do you think it is _________.

Table 1. Attitudes about sex before marriage

premarsx	n
always wrong	357
almost always wrong	122
wrong only sometimes	258
not wrong at all	1,378
Total	2,115

The number of respondents who answered this survey question.

Table 1. Attitudes about sex before marriage

premarsx	n
always wrong	357
almost always wrong	122
wrong only sometimes	258
not wrong at all	1,378
Total	2,115

The number of respondents who said pre-marital sex was “wrong only sometimes.”

Are women more likely than men to say premarital sex is “not wrong at all”?

Table 2. Attitudes about sex before marriage by gender

premarsx	male	female
always wrong	4,159	7,116
almost always wrong	1,499	2,388
wrong only sometimes	3,904	4,792
not wrong at all	10,672	11,086
Total	20,234	25,382

RELATIVE FREQUENCY DISTRIBUTION

A table showing the proportion or percentage for each value of a variable.

Proportions are between 0 and 1.0.

Proportion = count (f) / total number of cases (N).

Percentages are between 0 and 100.

Percentage = proportion × 100.

Tab A
Tab B

Table 2. Attitudes about sex before marriage by gender

	Men		Women
premarsx	n	%	n	%
always wrong	4,159	21	7,116	28
almost always wrong	1,499	7	2,388	9
wrong only sometimes	3,904	19	4,792	19
not wrong at all	10,672	53	11,086	44
Total	20,234	100	25,382	100

$\frac{\color{mathGreen}{11{,}086}}{\color{mathOrange}{25{,}382}} = 0.4367 \times 100 = \color{mathRed}{43.7\%}$

TIP: Total of a % column should always sum to 100!

Table 2. Attitudes about sex before marriage by gender

	Men		Women
premarsx	n	%	n	%
always wrong	4,159	21	7,116	28
almost always wrong	1,499	7	2,388	9
wrong only sometimes	3,904	19	4,792	19
not wrong at all	10,672	53	11,086	44
Total	20,234	100	25,382	100

A greater proportion of men (53%) than women (44%) say premarital sex is “not wrong at all.”

CUMULATIVE FREQUENCY DISTRIBUTION

The number or percentage of observations at or below a given category.

Table 3. Attitudes about sex before marriage, with cumulative percentages

premarsx	n	%	cumulative %
always wrong	357	17	17
almost always wrong	122	6	23
wrong only sometimes	258	12	35
not wrong at all	1,378	65	100
Total	2,115	100	175

${\color{mathGreen} 17} + {\color{mathOrange} 6} = {\color{mathRed} 23\%}$

RATES

$\frac{Actual\;occurrences}{possible\;occurrences}$

Examples:

Canada’s divorce rate decreased from 12.7 per 1,000 in 1991 to 5.6 per 1,000 in 2020.
The 2021 suicide rate of 14.8 per 100,000 population for middle aged Canadians (30-59 years old) was the highest of any age group.
Canada’s total fertility rate reached a new low in 2023 of 1.26 children per woman.

Nominal variables:
can have frequency distributions, cannot have cumulative frequency distributions

Ordinal:
can have frequency distributions and cumulative frequency distributions

Interval-ratio:
can have frequency distributions, cumulative frequency distributions, and rates

A bar graph is used:
for nominal or ordinal variables,

to show frequencies or percentages,

using separated rectangles, with height proportional
to the frequency or percentage.

A histogram is used:
for interval-ratio variables,

to show frequencies or percentages,

using separated rectangles, with height proportional
to the frequency or percentage.

A line graph is used:
for interval-ratio variables,

to show frequencies or percentages,

joining by category the frequency or average with a line.

A statistical map is used:
for interval-ratio variables,

to show geographical variations, often in ratios,

using variation in color or hue.

Central Tendency

MEAN
MEDIAN
MODE

OUTLIER
MOVING AVERAGE
PERCENTILE

BIMODAL
SYMMETRICAL DISTRIBUTION
POSITIVELY SKEWED DISTRIBUTION
NEGATIVELY SKEWED DISTRIBUTION

Objectives

Explain the importance of measures of central tendency.
Calculate and interpret the mean, the median, and the mode.
Identify the relative strengths and weaknesses of the three measures.
Determine and explain the shape of a distribution.

Summary statistics

We use summary statistics to find out what is TYPICAL in a distribution.

Mean

MEAN

The arithmetic average obtained by adding up all the scores and dividing by the total number of scores.

most commonly used measure of central tendency,
it’s weakness is that it is sensitive to outliers (extreme scores in a distribution)

Finding the mean in a list: $7, 4, 2, 8, 0, 9, 5$

Add all observations together: $7 + 4 + 2 + 8 + 0 + 9 + 5 = 35$
Divide the sum by the number of observations: $\frac{35}{7} = 5$

Family ID	Annual Income (CAD)
F01	$48,000
F02	$52,000
F03	$45,000
F04	$50,000
F05	$53,000
F06	$49,000
F07	$46,000
F08	$51,000
F09	$175,000
F10	$250,000

Most families in this sample earn between $45K–53K, but two high-income households push the average far above what’s typical.

Median

MEDIAN

The arithmetic average obtained by adding up all the scores and dividing by the total number of scores.

The median is the value at the 50th percentile in a cumulative frequency distribution.

PERCENTILE

A score below which a specific percentage of the distribution falls.

Median: Calculation

Tab A
Tab B
Tab C

Finding the median in a list with an odd number of observations:

$7, 2, 1, 3, 4, 1, 5, 9, 2$

Put the list in order: $1, 1, 2, 2, 3, 4, 5, 7, 9$
Pick the center number: $3$

Finding the median in a list with an even number of observations:

$2, 0, 1, 2, 5, 1, 3, 1$

Put the list in order: $0, 1, 1, 1, 2, 2, 3, 5$
Add the two center numbers & divide by 2: $\frac{1 + 2}{2} = 1.5$

Mode

MODE

Category or score with the highest frequency (or percentage) in a distribution.

BIMODEL

Two values or categories with the highest frequency.

Mode: Calculation

Tab A
Tab B
Tab C

Finding the mode in a list: $7, 2, 1, 3, 4, 1, 5, 1, 2 $

Put the list in order: $1, 1, 1, 2, 2, 3, 4, 5, 7$
Pick the most frequent number: $1$